绝尘
a3835e2acb
- 新建 df-ai/openai_helpers.rs(231行): apply_openai_sse纯函数 + 13 struct(OpenAi*字段pub(crate)) - openai_compat.rs 809→597: 删抽离+use引入, Provider impl保留(impl块约束: new/convert_request/parse_tool_calls/LlmProvider trait) - lib.rs: pub mod openai_helpers 主代兜底: cargo check --workspace 0 + test df-ai 119 strategy: 核心库自底向上, impl块约束(Provider方法保留只抽纯函数/类型), 对齐anthropic_helpers模式 git add指定(df-ai/*) 协议适配双拆完整: anthropic_compat + openai_compat 均helpers抽离
598 lines
25 KiB
Rust
598 lines
25 KiB
Rust
//! OpenAI 兼容 Provider — 通过 /v1/chat/completions 端点实现
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//!
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//! 覆盖: OpenAI / GLM (open.bigmodel.cn) / DeepSeek / Claude OpenAI 兼容模式
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//! 支持: 同步调用 + SSE 流式 + Function Calling / Tool Use
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use std::time::Duration;
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use async_trait::async_trait;
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use eventsource_stream::Eventsource;
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use futures::StreamExt;
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use reqwest::Client;
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use tracing::{debug, error, warn};
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use crate::provider::{
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CompletionRequest, CompletionResponse, LlmProvider, StreamResult, TokenUsage, ToolCall,
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};
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// ChatMessage 仅单测构造 CompletionRequest 用，避免非 test 构建的 unused import 警告。
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#[cfg(test)]
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use crate::provider::ChatMessage;
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use crate::retry::{
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retry_with_backoff, AttemptOutcome, is_reqwest_error_retryable, is_status_retryable,
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};
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// 协议数据结构（OpenAiRequest / OpenAiResponse / OpenAiStreamChunk …）与 SSE 解析纯函数
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// apply_openai_sse 抽离到 openai_helpers.rs，结构对齐 anthropic_helpers.rs。
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// Rust impl 块不可跨文件，Provider struct + impl 仍留本文件。
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use crate::openai_helpers::{
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apply_openai_sse, OpenAiMessage, OpenAiRequest, OpenAiResponse, OpenAiToolCallResp,
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};
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// ============================================================
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// OpenAI Compat Provider
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// ============================================================
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/// OpenAI 兼容 LLM Provider
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pub struct OpenAICompatProvider {
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client: Client,
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api_key: String,
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base_url: String,
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default_model: String,
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}
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impl OpenAICompatProvider {
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/// 创建 Provider
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///
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/// - `base_url`: 如 "https://api.openai.com", "https://open.bigmodel.cn/api/paas", "https://api.deepseek.com"
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/// - `api_key`: API 密钥
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/// - `default_model`: 默认模型名称
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pub fn new(base_url: impl Into<String>, api_key: impl Into<String>, default_model: impl Into<String>) -> Self {
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// SW-260618-10: reqwest Client 构建抽 crate::build_provider_client（与 Anthropic 共用 DRY）。
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// connect_timeout/回退策略集中此处，未来改一处即可（见 lib.rs::build_provider_client）。
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let client = crate::build_provider_client();
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Self {
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client,
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api_key: api_key.into(),
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base_url: base_url.into(),
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default_model: default_model.into(),
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}
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}
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/// 构建完整 API URL
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///
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/// 智能拼接，兼容三种 base_url 约定：
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/// - 已含完整端点（…/chat/completions）→ 直接用
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/// - 已含版本段（…/v1 …/v4 等，如 GLM 的 /api/paas/v4）→ 补 /chat/completions
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/// - 仅域名无版本（如 api.openai.com / api.deepseek.com）→ 补 /v1/chat/completions（OpenAI 约定）
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fn chat_url(&self) -> String {
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let base = self.base_url.trim_end_matches('/');
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if base.ends_with("/chat/completions") {
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return base.to_string();
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}
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if Self::ends_with_version(base) {
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return format!("{}/chat/completions", base);
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}
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format!("{}/v1/chat/completions", base)
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}
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/// base_url 是否以 `/v<数字>` 结尾（如 /v1 /v4）
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fn ends_with_version(base: &str) -> bool {
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match base.rsplit_once('/') {
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Some((_, last)) if last.starts_with('v') && last.len() > 1 => {
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last[1..].bytes().all(|b| b.is_ascii_digit())
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}
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_ => false,
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}
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}
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/// 构建 embeddings API URL（与 chat_url 同套智能拼接规则）
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fn embed_url(&self) -> String {
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let base = self.base_url.trim_end_matches('/');
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if base.ends_with("/embeddings") {
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return base.to_string();
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}
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if Self::ends_with_version(base) {
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return format!("{}/embeddings", base);
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}
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format!("{}/v1/embeddings", base)
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}
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/// 将通用请求转换为 OpenAI 格式
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fn convert_request(&self, req: CompletionRequest) -> OpenAiRequest {
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let model = if req.model.is_empty() {
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self.default_model.clone()
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} else {
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req.model
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};
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let messages: Vec<OpenAiMessage> = req
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.messages
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.into_iter()
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.map(|m| {
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let role = match m.role {
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crate::provider::MessageRole::System => "system",
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crate::provider::MessageRole::User => "user",
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crate::provider::MessageRole::Assistant => "assistant",
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crate::provider::MessageRole::Tool => "tool",
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};
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// F-260614-05 Phase 2a: 多模态 content（须在 move m.tool_calls 之前算，借用 m）。
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// 含图消息走 content 数组（text/image_url）；纯文本走字符串简写
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// （保持与现有纯文本端点零回归）。image_url 支持 data URI（base64）与 http(s) URL。
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let content = if m.has_image() {
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let parts: Vec<serde_json::Value> = m
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.flattened_parts()
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.into_iter()
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.map(|p| match p {
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crate::provider::ContentPart::Text { text } => serde_json::json!({
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"type": "text",
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"text": text,
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}),
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crate::provider::ContentPart::Image { url, base64, media_type, alt: _ } => {
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let final_url = match (base64, url, media_type) {
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(Some(b), _, Some(mt)) => {
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format!("data:{};base64,{}", mt, b)
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}
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(None, Some(u), _) => u,
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// 完整性兜底：当前 image_base64 构造器强制 media_type:Some，
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// image_url 构造器提供 url:Some，二者分别命中上两个分支；
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// 此分支仅在 parts 来源被外部直接构造且字段均缺时才可达
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//（如 url:None+base64:None 或 base64:Some+media_type:None）。
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// 退化为空串（OpenAI 对空 image_url.url 会 400），
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// 由调用方保证 parts 合法性，provider 层不做静默伪造。
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_ => String::new(),
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};
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serde_json::json!({
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"type": "image_url",
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"image_url": { "url": final_url },
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})
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}
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})
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.collect();
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serde_json::Value::Array(parts)
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} else {
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serde_json::Value::String(m.content.clone())
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};
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let tool_calls = m.tool_calls.map(|calls| {
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calls
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.into_iter()
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.map(|tc| {
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serde_json::json!({
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"id": tc.id,
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"type": tc.call_type,
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"function": {
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"name": tc.function.name,
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"arguments": tc.function.arguments,
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}
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})
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})
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.collect()
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});
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OpenAiMessage {
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role: role.to_string(),
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content,
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tool_call_id: m.tool_call_id,
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tool_calls,
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reasoning_content: m.reasoning_content,
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}
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})
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.collect();
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let tools = req.tools.map(|defs| {
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defs.into_iter()
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.map(|d| serde_json::to_value(d).unwrap_or_default())
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.collect()
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});
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OpenAiRequest {
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model,
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messages,
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temperature: req.temperature,
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max_tokens: req.max_tokens,
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stream: req.stream,
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tools,
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tool_choice: req.tool_choice,
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reasoning_content: req.reasoning_content,
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// 流式请求末 chunk 带 usage（同步调用 complete 不需要）
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stream_options: if req.stream {
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Some(serde_json::json!({ "include_usage": true }))
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} else {
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None
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},
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}
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}
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/// 解析同步响应中的工具调用
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fn parse_tool_calls(calls: Vec<OpenAiToolCallResp>) -> Vec<ToolCall> {
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calls
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.into_iter()
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.map(|c| ToolCall::new(c.id, c.function.name, c.function.arguments))
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.collect()
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}
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}
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#[async_trait]
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impl LlmProvider for OpenAICompatProvider {
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/// 文本嵌入: POST /v1/embeddings(OpenAI 兼容,智谱/阿里百炼/OpenAI 通用)
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async fn embed(&self, model: &str, texts: Vec<String>) -> anyhow::Result<Vec<Vec<f32>>> {
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#[derive(serde::Deserialize)]
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struct EmbedData { embedding: Vec<f32>, index: usize }
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#[derive(serde::Deserialize)]
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struct EmbedResponse { data: Vec<EmbedData> }
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let resp = self
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.client
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.post(self.embed_url())
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.header("Authorization", format!("Bearer {}", self.api_key))
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.header("Content-Type", "application/json")
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.json(&serde_json::json!({ "model": model, "input": texts }))
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.send()
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.await?;
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if !resp.status().is_success() {
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let status = resp.status();
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let body = resp.text().await.unwrap_or_default();
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anyhow::bail!("Embedding API 错误 {}: {}", status, body);
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}
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let mut body: EmbedResponse = resp.json().await?;
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// 按 index 排序保证与输入顺序一致(API 不保证返回顺序)
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body.data.sort_by_key(|d| d.index);
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Ok(body.data.into_iter().map(|d| d.embedding).collect())
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}
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async fn complete(&self, request: CompletionRequest) -> anyhow::Result<CompletionResponse> {
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let mut req = request;
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req.stream = false;
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let openai_req = self.convert_request(req);
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debug!(model = %openai_req.model, "OpenAI 同步调用");
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// 指数退避重试(B-260616-07): 包裹 send + 状态码判定。
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// 单请求 60s timeout 保持不变(FR-R4)，重试是额外层: 3 次 × 60s 最坏 180s，
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// 由 retry_with_backoff 内部 30s 总预算主动止损。
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let label = format!("OpenAI[{}]", openai_req.model);
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retry_with_backoff(&label, move |_| {
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let client = self.client.clone();
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let url = self.chat_url();
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let api_key = self.api_key.clone();
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let openai_req = openai_req.clone();
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async move {
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// send
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let resp = client
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.post(url)
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.header("Authorization", format!("Bearer {}", api_key))
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.header("Content-Type", "application/json")
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.timeout(Duration::from_secs(60))
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.json(&openai_req)
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.send()
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.await;
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let resp = match resp {
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Ok(r) => r,
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Err(e) => {
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if is_reqwest_error_retryable(&e) {
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return AttemptOutcome::Retryable(format!("请求失败(可重试): {}", e));
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}
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return AttemptOutcome::Fatal(format!("请求失败(不可重试): {}", e));
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}
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};
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// 状态码判定
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if !resp.status().is_success() {
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let status = resp.status().as_u16();
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let body = resp.text().await.unwrap_or_default();
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let msg = format!("LLM API 错误 {}: {}", status, body);
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if is_status_retryable(status) {
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warn!(%status, "OpenAI 同步调用可重试状态码");
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return AttemptOutcome::Retryable(msg);
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}
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error!(%status, %body, "LLM API 调用失败(不可重试)");
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return AttemptOutcome::Fatal(msg);
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}
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// body 解析: 解析错属 Fatal(响应已成功送达，重试也会因同样格式失败)
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let body: OpenAiResponse = match resp.json().await {
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Ok(b) => b,
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Err(e) => return AttemptOutcome::Fatal(format!("响应解析失败: {}", e)),
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};
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let choice = match body.choices.into_iter().next() {
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Some(c) => c,
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None => return AttemptOutcome::Fatal("LLM 响应无 choices".to_string()),
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};
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let text = choice.message.content.unwrap_or_default();
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let tool_calls = choice.message.tool_calls.map(Self::parse_tool_calls);
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let usage = body.usage.map(|u| TokenUsage {
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prompt_tokens: u.prompt_tokens,
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completion_tokens: u.completion_tokens,
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total_tokens: u.total_tokens,
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}).unwrap_or(TokenUsage {
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prompt_tokens: 0,
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completion_tokens: 0,
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total_tokens: 0,
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});
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AttemptOutcome::Ok(CompletionResponse {
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text,
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model: body.model,
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usage,
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tool_calls,
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reasoning_content: choice.message.reasoning_content,
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})
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}
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})
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.await
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}
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async fn stream(&self, request: CompletionRequest) -> anyhow::Result<StreamResult> {
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let mut req = request;
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req.stream = true;
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let openai_req = self.convert_request(req);
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debug!(model = %openai_req.model, "OpenAI 流式调用");
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let resp = self
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.client
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.post(self.chat_url())
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.header("Authorization", format!("Bearer {}", self.api_key))
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.header("Content-Type", "application/json")
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.json(&openai_req)
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.send()
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.await?;
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if !resp.status().is_success() {
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let status = resp.status();
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let body = resp.text().await.unwrap_or_default();
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error!(%status, %body, "LLM 流式 API 调用失败");
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anyhow::bail!("LLM 流式 API 错误 {}: {}", status, body);
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}
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// 累积流式 usage：开 include_usage 后，末段正常 chunk（finish_reason）及额外 usage-only chunk（choices=[]）都带 usage。
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// usage 解析/累积逻辑抽到 apply_openai_sse 纯函数，便于单测；此处闭包只负责传 data 与传递 last_usage。
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let mut last_usage: Option<TokenUsage> = None;
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let stream = resp
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.bytes_stream()
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.eventsource()
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.map(move |event| match event {
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Ok(event) => Ok(apply_openai_sse(&event.data, &mut last_usage)),
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Err(e) => {
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// 保留 #[source] 因果链: anyhow!("...{}", e) 仅把 e 的 Display 塞进 message,
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// 丢掉 source(无法 downcast/遍历)。改用 Error::from(e).context(...):
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// Display 不变(仍为 "SSE 流错误: {e}"), 且 e 作为 .source() 可追溯。
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// 顺序: 先 format(e) 构造 context 文案, 再 Error::from(e) move e 进 source。
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let ctx = format!("SSE 流错误: {}", e);
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error!("{}", ctx);
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Err(anyhow::Error::from(e).context(ctx))
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}
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});
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Ok(Box::pin(stream))
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}
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fn name(&self) -> &str {
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&self.default_model
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}
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fn endpoint(&self) -> String {
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self.chat_url()
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}
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}
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// ============================================================
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// 单测（不发真实 HTTP，喂构造的 SSE data 字符串序列）
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// ============================================================
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#[cfg(test)]
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mod tests {
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use super::*;
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/// 辅助：构造普通文本 delta chunk 的 SSE data
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fn text_chunk(content: &str, finish_reason: Option<&str>) -> String {
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let fr = match finish_reason {
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Some(r) => format!(", \"finish_reason\": \"{}\"", r),
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None => String::from(", \"finish_reason\": null"),
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};
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format!(
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r#"{{"choices":[{{"delta":{{"content":"{}"}}{}}}]}}"#,
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content, fr
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)
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}
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/// 辅助：构造带 usage 的 chunk（choices 为空 → usage-only 末 chunk，对应 include_usage）
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fn usage_only_chunk(prompt: u32, completion: u32) -> String {
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format!(
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r#"{{"choices":[],"usage":{{"prompt_tokens":{},"completion_tokens":{},"total_tokens":{}}}}}"#,
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prompt,
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completion,
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prompt + completion
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)
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}
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/// 辅助：构造既有 content 又带 usage 的末段 chunk（部分兼容端点会把 usage 挂到正常末 chunk 上）
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fn text_chunk_with_usage(content: &str, finish_reason: &str, prompt: u32, completion: u32) -> String {
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format!(
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r#"{{"choices":[{{"delta":{{"content":"{}"}},"finish_reason":"{}"}}],"usage":{{"prompt_tokens":{},"completion_tokens":{},"total_tokens":{}}}}}"#,
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content,
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finish_reason,
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prompt,
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completion,
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prompt + completion
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)
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}
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/// 多 chunk 文本流后，末 chunk 携带 usage（include_usage 覆盖语义）
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#[test]
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fn openai_sse_multi_chunk_with_terminal_usage() {
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let mut acc: Option<TokenUsage> = None;
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// 1) 首个文本增量，无 usage
|
||
let c = apply_openai_sse(&text_chunk("Hello", None), &mut acc);
|
||
assert_eq!(c.delta, "Hello");
|
||
assert!(!c.finished);
|
||
assert!(c.usage.is_none());
|
||
assert!(acc.is_none(), "无 usage 的 chunk 不应改累加器");
|
||
|
||
// 2) 第二个文本增量
|
||
let c = apply_openai_sse(&text_chunk(" world", None), &mut acc);
|
||
assert_eq!(c.delta, " world");
|
||
assert!(!c.finished);
|
||
assert!(acc.is_none());
|
||
|
||
// 3) 末段正常 chunk 带 finish_reason=stop（仍是文本 delta，不带 usage）
|
||
let c = apply_openai_sse(&text_chunk("", Some("stop")), &mut acc);
|
||
assert!(c.finished);
|
||
assert_eq!(c.delta, "");
|
||
assert!(acc.is_none(), "此 chunk 无 usage 字段，累加器仍为 None");
|
||
|
||
// 4) usage-only chunk（choices=[]）携带累计 usage → 覆盖累加器
|
||
let c = apply_openai_sse(&usage_only_chunk(12, 34), &mut acc);
|
||
assert!(!c.finished);
|
||
assert!(c.usage.is_none(), "非 [DONE] chunk 不带出 usage");
|
||
let acc = acc.expect("累加器应已被 usage-only chunk 覆盖写入");
|
||
assert_eq!(acc.prompt_tokens, 12);
|
||
assert_eq!(acc.completion_tokens, 34);
|
||
assert_eq!(acc.total_tokens, 46);
|
||
}
|
||
|
||
/// usage 挂在正常末段 chunk（含 finish_reason）上，而非独立 usage-only chunk
|
||
#[test]
|
||
fn openai_sse_usage_on_terminal_text_chunk() {
|
||
let mut acc: Option<TokenUsage> = None;
|
||
let c = apply_openai_sse(&text_chunk_with_usage("", "stop", 100, 200), &mut acc);
|
||
assert!(c.finished);
|
||
assert!(c.usage.is_none(), "非 [DONE] 不带出 usage，仅覆盖累加器");
|
||
let acc = acc.expect("末段 chunk 的 usage 应已覆盖累加器");
|
||
assert_eq!(acc.prompt_tokens, 100);
|
||
assert_eq!(acc.completion_tokens, 200);
|
||
assert_eq!(acc.total_tokens, 300);
|
||
}
|
||
|
||
/// [DONE] 时 take() 带出累积 usage，且取走后累加器清空
|
||
#[test]
|
||
fn openai_sse_done_takes_accumulated_usage() {
|
||
let mut acc: Option<TokenUsage> = None;
|
||
apply_openai_sse(&text_chunk("x", None), &mut acc);
|
||
apply_openai_sse(&usage_only_chunk(5, 7), &mut acc);
|
||
|
||
let c = apply_openai_sse("[DONE]", &mut acc);
|
||
assert!(c.finished);
|
||
let u = c.usage.expect("[DONE] 应带出累积 usage");
|
||
assert_eq!(u.prompt_tokens, 5);
|
||
assert_eq!(u.completion_tokens, 7);
|
||
assert_eq!(u.total_tokens, 12);
|
||
assert!(acc.is_none(), "take() 后累加器应清空");
|
||
}
|
||
|
||
/// 无 usage 的流：[DONE] 时 usage 字段为 None
|
||
#[test]
|
||
fn openai_sse_done_without_usage() {
|
||
let mut acc: Option<TokenUsage> = None;
|
||
apply_openai_sse(&text_chunk("hi", None), &mut acc);
|
||
let c = apply_openai_sse("[DONE]", &mut acc);
|
||
assert!(c.finished);
|
||
assert!(c.usage.is_none(), "全程无 usage 时 [DONE] usage 应为 None");
|
||
assert!(acc.is_none());
|
||
}
|
||
|
||
/// 后续 usage chunk 覆盖先前 usage（多轮 / 重发场景）
|
||
#[test]
|
||
fn openai_sse_later_usage_overrides_earlier() {
|
||
let mut acc: Option<TokenUsage> = None;
|
||
apply_openai_sse(&usage_only_chunk(1, 1), &mut acc);
|
||
apply_openai_sse(&usage_only_chunk(50, 60), &mut acc);
|
||
let c = apply_openai_sse("[DONE]", &mut acc);
|
||
let u = c.usage.unwrap();
|
||
assert_eq!(u.prompt_tokens, 50, "末 usage 应覆盖前值");
|
||
assert_eq!(u.completion_tokens, 60);
|
||
assert_eq!(u.total_tokens, 110);
|
||
}
|
||
|
||
/// finish_reason=length（max_tokens 截断）按正常终止处理
|
||
#[test]
|
||
fn openai_sse_length_finish_reason_treated_as_finished() {
|
||
let mut acc: Option<TokenUsage> = None;
|
||
let c = apply_openai_sse(&text_chunk("...", Some("length")), &mut acc);
|
||
assert!(c.finished, "length 应视为正常终止");
|
||
assert!(acc.is_none());
|
||
}
|
||
|
||
/// 非法 JSON data → 返回空 chunk，不 panic、不改累加器
|
||
#[test]
|
||
fn openai_sse_malformed_json_yields_empty_chunk() {
|
||
let mut acc: Option<TokenUsage> = None;
|
||
let c = apply_openai_sse("not a json", &mut acc);
|
||
assert_eq!(c.delta, "");
|
||
assert!(!c.finished);
|
||
assert!(c.usage.is_none());
|
||
assert!(acc.is_none());
|
||
}
|
||
|
||
/// tool_calls 增量解析
|
||
#[test]
|
||
fn openai_sse_tool_call_delta() {
|
||
let mut acc: Option<TokenUsage> = None;
|
||
let data = r#"{"choices":[{"delta":{"tool_calls":[{"index":0,"id":"call_1","function":{"name":"get_weather","arguments":"{\"q\":"}}]}}]}"#;
|
||
let c = apply_openai_sse(data, &mut acc);
|
||
assert!(acc.is_none());
|
||
let tcs = c.tool_calls.expect("应有 tool_calls 增量");
|
||
assert_eq!(tcs.len(), 1);
|
||
assert_eq!(tcs[0].index, 0);
|
||
assert_eq!(tcs[0].id.as_deref(), Some("call_1"));
|
||
assert_eq!(tcs[0].function_name.as_deref(), Some("get_weather"));
|
||
assert_eq!(tcs[0].function_arguments.as_deref(), Some("{\"q\":"));
|
||
assert!(!c.finished);
|
||
}
|
||
|
||
// ---------- F-260614-05 Phase 2a 多模态 convert_request ----------
|
||
|
||
/// 含图消息 → content 数组（text + image_url data URI）；纯文本 → 字符串简写
|
||
#[test]
|
||
fn openai_convert_multimodal_content() {
|
||
let provider = OpenAICompatProvider::new("https://api.openai.com", "k", "gpt-4o");
|
||
let req = CompletionRequest {
|
||
model: "gpt-4o".into(),
|
||
messages: vec![ChatMessage::user_parts(
|
||
"看图",
|
||
vec![
|
||
crate::provider::ContentPart::image_base64("image/png", "iVBOR"),
|
||
crate::provider::ContentPart::image_url("https://x/a.png"),
|
||
],
|
||
)],
|
||
temperature: None,
|
||
max_tokens: None,
|
||
stream: false,
|
||
tools: None,
|
||
tool_choice: None,
|
||
reasoning_content: None,
|
||
};
|
||
let out = provider.convert_request(req);
|
||
let msg = &out.messages[0];
|
||
// content 是数组：[text "看图", image_url(data URI), image_url(http url)]
|
||
let arr = msg.content.as_array().expect("含图 → content 数组");
|
||
assert_eq!(arr.len(), 3);
|
||
assert_eq!(arr[0]["type"], "text");
|
||
assert_eq!(arr[0]["text"], "看图");
|
||
assert_eq!(arr[1]["type"], "image_url");
|
||
assert_eq!(
|
||
arr[1]["image_url"]["url"],
|
||
"data:image/png;base64,iVBOR"
|
||
);
|
||
assert_eq!(arr[2]["image_url"]["url"], "https://x/a.png");
|
||
}
|
||
|
||
/// 纯文本消息 → content 仍是字符串简写（无图不数组化，对齐纯文本端点兼容）
|
||
#[test]
|
||
fn openai_convert_text_only_remains_string() {
|
||
let provider = OpenAICompatProvider::new("https://api.openai.com", "k", "gpt-4o");
|
||
let req = CompletionRequest {
|
||
model: "gpt-4o".into(),
|
||
messages: vec![ChatMessage::user("hello")],
|
||
temperature: None,
|
||
max_tokens: None,
|
||
stream: false,
|
||
tools: None,
|
||
tool_choice: None,
|
||
reasoning_content: None,
|
||
};
|
||
let out = provider.convert_request(req);
|
||
let msg = &out.messages[0];
|
||
assert_eq!(msg.content, serde_json::Value::String("hello".into()));
|
||
}
|
||
}
|